Spindle Vibration Signal Research Articles

Spindle Health Diagnosis Based on Analytic Wavelet Enveloping

A new diagnostic technique for identifying structural defects in spindles was developed based on the analytic wavelet transform. The new technique extracts defect-induced impulses from the spindle vibration signal and constructs their envelopes in a single step, eliminating the need for intermediate operations as traditionally required. Theoretical background of the analytic wavelet transform was first introduced, and numerical simulation was then conducted on a synthetic signal. The result was subsequently compared with vibration signals measured on a spindle test bed. It was confirmed that the developed technique is effective in detecting defect-induced impulses buried in the spindle vibration signals that otherwise were undetectable using the traditional spectral techniques

Vibration modelling with fast Gaussian wavelet algorithm

Vibration signals are considered as nonstationary signals with transients. Conventional harmonic Fourier analysis finds it difficult to model the vibration signals. In this paper, a novel approach using the global Fourier transforms and local wavelet analysis is presented for vibration analysis and modelling. Time–frequency wavelet analysis has been proven a useful tool for the detection of vibration transients. However, current algorithms with discrete or continuous wavelet transforms for vibration analysis are either low resolution of features or very time consuming. We developed a fast Gaussian wavelet algorithm with very narrow band-pass filtering technique. The time–frequency maps with high frequency resolution enable us to observe the evolution in time of significant frequencies identified by global Fourier analysis, so that the transients and the regular signals can be distinguished. These regular significant frequencies are selected to be the basis of vibration modelling. The coefficients of the model are identified by a least-squares algorithm, which ensures that the error is minimised. To demonstrate this approach a machine spindle vibration signal is analysed, and the main features of the vibration signal are extracted, which are useful for system monitoring and further analysis.